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Tensorflow simple verification code recognition application, tensorflow Verification Code
Simple Tensorflow verification code recognition application for your reference. The specific content is as follows:
1. Tensorflow Installation MethodI will not go into details here.
2. Training setAs well as testing and the follow
TensorFlow is used for simple linear regression and gradient descent examples. tensorflow gradient
Linear regression is supervised learning. Therefore, the method and supervised learning should be the same. First, a training set is given and a linear function is learned based on the training set, then, test whether the function is trained (that is, whether the function is sufficient to fit the training set
Preface:
TensorFlow There are many basic concepts to understand, the best way is to go to the official website followed by the tutorial step by step, there are some translated version, compared to see to help understand: tensorflow1.0 document translation text:
One, the necessary process of building and executing the calculation diagram
1,graph (Figure calculation): see TF. Graph classUsing TensorFlow to t
), variables (Variable). lesson three TensorFlow linear regression and simple use of classifications. The fourth lesson Softmax, cross-entropy (cross-entropy), dropout, and the introduction of various optimizations in TensorFlow. Fifth Lesson, CNN, and CNN to solve the problem of mnist classification. The sixth lesson uses Tensorboard to visualize the structure a
TensorFlow variable management details, tensorflow variable details
I. TensorFlow variable Management
1. TensorFLow also provides the tf. get_variable function to create or obtain variables. When tf. variable is used to create variables, its functions are basically equivalent to tf. Variable. The initialization method
Use tensorflow to build CNN and tensorflow to build cnn
Convolutional Neural Networks Convolutional Neural Network (CNN) transfers the data of an image to CNN. The original coating is composed of RGB, And then CNN thickened the thickness and the length and width become smaller, each layer is stretched to form a classifier.
There are several important concepts in CNN:
Stride
Padding
Pooling
Stride i
the result.Call TensorFlow the process is very fun, but also very convenient. So, why is it that TensorFlow can identify what the picture is all of a sudden? TensorFlow's official website gives the following answers:Www.tensorflow.org/tutorials/image_recognitionIt is necessary to note that TensorFlow's image recognition classification can be submitted to the ser
TensorFlow requires Python 3.5/3.6 64bit version:Specific installation methods can be viewed: https://www.tensorflow.org/install/install_windows Enter Python at the command prompt to start and view the current version: To view the specific version information, enter:1 python-v Download the new 64bit version of Python for installation.Windows Python3.6.5 64bit:https://www.python.org/ftp/python/3.6.5/python-3.6.5-amd64.exeWindows
1. Overview
As with the old version of TensorFlow, the model needs to be saved, and this preservation is cyclical. Because in many cases the gradient will swing around the local minimum, that is to say, in many cases, the last training model is not necessarily optimal.
2. Save the Model
We can create a location where the checkpoint is saved when we build the model, and we can start by creating a folder with the following command.
You can add paramet
If you write a system, you will often use the column management
Column Classification Multi-level more need to achieve unlimited class classification, the code is as follows
One, the use of the drop-down menu
/* Infinite class * * logical recursive Get type *hid ancestor column ID *step subordinate column prefix *tid seleted option ID */function logicgettypelist ($datatable = ' Lanmu_class ', $hid =0, $st
result, the biggest one is usually the forecast result}
Tensorflowinferenceinterface Reference:
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/android/java/org/tensorflow/contrib/ Android/tensorflowinferenceinterface.java
Java APIs and TensorFlow
=Tf.reduce_mean (Tf.abs (A)) L2_a_loss=Tf.reduce_mean (Tf.square (A)) E1_term=tf.multiply (elastic_p1,l1_a_loss) e2_term=tf.multiply (Elastic_p2,l2_a_loss)#here A is an irregular shape that corresponds to the array form of the 3,1 loss also expands the arrays formLoss=tf.expand_dims (Tf.add (Tf.add (Tf.reduce_mean (Tf.square (y_target-model_out)), e1_term), e2_term), 0)#Initialize Variablesinit=Tf.global_variables_initializer () sess.run (init)#Gradient Descentmy_opt=Tf.train.GradientDescentOpti
In general, there are two functions for printing tensorflow variables:tf.trainable_variables () and Tf.all_variables ()The difference is:Tf.trainable_variables () refers to the variables that need to be trainedTf.all_variables () refers to all variables
In general, we are more concerned with training variables that need to be trained:It is important to note that the entire graph is initialized when the variable name is output
First, print the name of
. start_queue_runners (coord = coord) try: while not coord. should_stop (): e_val, l_val = sess. run ([example_batch, label_batch]) print e_val, l_val limit t tf. errors. outOfRan GeError: print ('epochs Complete! ') Finally: coord. request_stop () coord. join (threads) coord. request_stop () coord. join (threads)
In iteration control, remember to add tf. initialize_local_variables (). The tutorial on the official website is not described. However, if the initialization is not performed, an erro
Pattern Recognition field Application machine learning scene is very many, handwriting recognition is one of the most simple digital recognition is a multi-class classification problem, we take this multi-class classification problem to introduce Google's latest open source TensorFlow framework, The content behind the deep learning will be presented and demonstra
Learning notes TF024: TensorFlow achieves Softmax Regression (Regression) Recognition of handwritten numbers, tf024softmax
TensorFlow implements Softmax Regression (Regression) to recognize handwritten numbers. MNIST (Mixed National Institute of Standards and Technology database), simple machine vision dataset, 28x28 pixels handwritten number, only grayscale value information, blank part is 0, handwriting a
TensorFlow implements the Softmax regression model, tensorflowsoftmax
I. Overview and complete code
Tensorflow encapsulates MNIST (MixedNational Institute of Standard and Technology database), a very simple machine vision dataset, and can directly load MNIST data into the expected format. this program uses Softmax Regression to train the classification model for
in the nth dimension (starting at 0). For example, label 0 will be represented as ([1,0,0,0,0,0,0,0,0,0,0]). Therefore, Mnist.train.labels is a digital matrix of [60000, 10].2.softmax regression test mnist1) Softmax regression modelClick to view a post in detail on Softmax regression:Simply put, Softmax regression is the generalization of logistic regression to multi-classification problem, when it is two classif
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